Every organization has some manual work in Master Data Management, which is defined as a company’s core data such as customer or employee data as well as product information. In the last few years, robotic process automation (RPA) has taken the center stage to enable companies to standardize, automate, and increase efficiency within their processes. In this blog, we will look at some of the possibilities that RPA offers to automate high-volume, time-consuming master data management tasks. Before we explain how RPA can enhance master data management, let us understand the concept of master data management and RPA, and the immense value these two provide when combined.
What is Master Data Management?
Master data management is the most critical data that a company holds. This data can be product or services information, employee data, or sensitive client data. Some of the examples of departments that constantly keep updating their master data include Sales, Marketing, and Customer Services. Master data is also important for product management and R&D departments since they work with the operations team to ensure the timely procurement of material required to develop new products.
Understanding the way your master data is assisting your business is the first step to know if and how RPA can be leveraged to improve efficiency and accuracy. If your data is complete and accurate, you can make a better decision, spend less time on re-work, comply with regulations, and invariably have higher process efficiency. Operational processes in master data management, such as creating, updating, or deleting, are the key contenders of RPA since these are important yet monotonous tasks that consume a lot of time. You should also consider other factors such as the number of changes and errors’ risks in the master data management process since not all of them are the right fit for automation.
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What is Robotic Process Automation?
Robotic process automation is a software bot that can automate mundane processes done by humans. It has been commonly implemented in several functions including Finance, HR, or IT. For a successful implementation of RPA, you should look for processes that are high in volume, structured, and repetitive. The bot integrates with the existing applications and works on the user interface to perform work such as screen scraping, sending emails, and executing defined workflows.
In processes involving unstructured data or complex logic, companies deploy intelligent automation, which embeds smart add-ons, such as chatbots, OCR, and machine learning. Some of the main advantages of RPA is the shorter deployment time and its licenses cost. In a normal scenario, RPA is implemented through a Center of Excellence (CoE), which includes both business and IT teams closely aligned with process specialists.
Unlike implementation of IT projects, RPA is a more business-centric, tailored solution. It promises a quick return on investment along with less implementation time among other benefits.
To successfully implement RPA, consider the following points:
- Proof of concept: to see how the bots will function in your business environment and scale it as needed.
- RPA-specific governance: a need to have a solution to govern software bots to understand the overall impact of RPA on the business.
- Robust RPA strategy: that includes business drivers, organizational vision as well as RPA sourcing and deployment.
Application Of RPA In Master Data Management
The finance function is taking the lead on RPA by using it to handle record-to-report, reconciliations, purchase-to-pay, and order-to-cash processes. Other departments such as Human Resources, IT, and procurement are following suit. For departments dealing with master data management, the best prospect for RPA is being identified using process mining.
Process mining allows business analysts to select key focus areas for business cases. It helps find potential disruptions caused by master data issues and tasks done by the human workforce that consume a lot of time and deviations. Typically, a mature process, which has standard processes are eligible for process automation. For processes that require human intervention and decision making, cognitive automation is used to optimize and reconfigure the service delivery model.
Other processes that include systems involving data handoffs with each other can also be automated through better integrations and APIs. But this is not as easy and smooth as it seems. The promise of an integrated IT ecosystem has been lurking for a while now, but in practice, companies still rely on legacy systems to perform critical functions. Add to it are siloed solutions and the use of archaic methods that does not alleviate manual human interventions. That is where RPA can save the day by allowing a seamless flow of data between systems through integration, thereby providing consistent master data operations within the company.
Due to its myriad benefits, RPA in master data management is becoming more relevant for organizations that have complexities in their varied master data ecosystem. To quickly realize the benefits of RPA, companies need to begin with RPA within master data management operations to take control of their data and harmonize multiple processes and standards.
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